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### packages ----------
library("tidyverse")
[37m── [1mAttaching packages[22m ────────────────────────────── tidyverse 1.2.1 ──[39m
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library("stringr")
dataset_PROSPERO2_final<-dataset_PROSPERO2_final %>%
mutate(Topic_gral=recode(Topic, c("cancer" = "Cancer")))
Error in mutate_impl(.data, dots) :
Evaluation error: Argument 2 must be named, not unnamed.
#### extraer CRDs del campo Citation con regex
prosperoID<-"[C][R][D]([0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9])"
dataset_raw$crds<-str_extract(dataset_raw$Citation, prosperoID)
curated_dataset<-read.csv(file="dataset_PROSPERO2_final.csv")
Error in read.table(file = file, header = header, sep = sep, quote = quote, :
more columns than column names


#### Collaborative Countries
#### no parece muy diferente al plot con unique!!!! :-(
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>%
#filter(., unique_country=="unique") %>%
filter(., tool!="No") %>%
filter(., new_unitofanalysis!="No") %>%
filter(., measure!="BMJ Critical Appraisal Tools") %>%
filter(., measure!="CEBM Critical Appraisal Tools") %>%
filter(., measure!="JBI Critical Appraisal Tools") %>%
filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")
collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
Unknown levels in `f`: Health education and health behavior research studies, Quantitative Research (Public Health Research)
ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,y=tool, fill=new_unitofanalysis, color=new_unitofanalysis, shape=measure, alpha=Freq_topics)) +
geom_count(aes(x=Topics_general,y=tool, fill=new_unitofanalysis, color=new_unitofanalysis, shape=measure,alpha=Freq_topics))+
scale_shape(name = "Assessment objective")+
scale_shape_manual(values=c(24,21,22,25))+
coord_flip()+
theme(axis.text.y=element_text(angle=0, size=6))+
theme(axis.text.x=element_text(angle=90, size=6, hjust=1))+
facet_wrap(~measure)+
theme(legend.position="none")
Scale for 'shape' is already present. Adding another scale for 'shape', which will replace the existing scale.

#### barplots
#### topic X measure AND unitOfAnalisys
#### UNIQUE COUNTRIES
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>%
filter(., unique_country=="unique") %>%
filter(., tool!="No") %>%
filter(., new_unitofanalysis!="No") %>%
filter(., measure!="Diagnostic Accuracy") %>%
filter(., measure!="BMJ Critical Appraisal Tools") %>%
filter(., measure!="CEBM Critical Appraisal Tools") %>%
filter(., measure!="JBI Critical Appraisal Tools") %>%
filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")
unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
Unknown levels in `f`: Health education and health behavior research studies, Quantitative Research (Public Health Research)
ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) +
geom_bar(stat='count', aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
coord_flip()+
theme(axis.text.x=element_text(size=6))+
theme(axis.text.y=element_text(size=10))+
facet_wrap(~measure)+
theme(legend.position="none")

#### COLLABORATIVE COUNTRIES
# unique_curated_dataset_and_tools<-curated_dataset_and_tools %>%
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>%
filter(str_detect(PROSPERO_country, ',')) %>%
filter(., tool!="No") %>%
filter(., new_unitofanalysis!="No") %>%
filter(., measure!="Diagnostic Accuracy") %>%
filter(., measure!="BMJ Critical Appraisal Tools") %>%
filter(., measure!="CEBM Critical Appraisal Tools") %>%
filter(., measure!="JBI Critical Appraisal Tools") %>%
filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")
collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
Unknown levels in `f`: Health education and health behavior research studies, Quantitative Research (Public Health Research)
ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) +
geom_bar(stat='count', aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics, drop = FALSE))+
scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
coord_flip()+
scale_y_continuous(limits = c(0, 500))+
theme(axis.text.x=element_text(size=6))+
theme(axis.text.y=element_text(size=10))+
facet_wrap(~measure)+
theme(legend.position="none")
Ignoring unknown aesthetics: drop


#### barplots
#### topic X measure AND unitOfAnalisys
#### UNIQUE COUNTRIES
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>%
filter(., unique_country=="unique") %>%
filter(., tool!="No") %>%
filter(., new_unitofanalysis!="No") %>%
filter(., measure!="Diagnostic Accuracy") %>%
filter(., measure!="BMJ Critical Appraisal Tools") %>%
filter(., measure!="CEBM Critical Appraisal Tools") %>%
filter(., measure!="JBI Critical Appraisal Tools") %>%
filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")
unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
Unknown levels in `f`: Health education and health behavior research studies, Quantitative Research (Public Health Research)
ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) +
geom_bar(stat='count', aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
#coord_flip()+
theme(axis.text.x=element_text(size=6))+
theme(axis.text.y=element_text(size=6))+
scale_x_discrete(labels = c("2011", "2012", "2013", "2014", "2015", "2016", "2017"))+
facet_wrap(~measure)+
theme(legend.position="none")

#### COLLABORATIVE COUNTRIES
# unique_curated_dataset_and_tools<-curated_dataset_and_tools %>%
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>%
filter(str_detect(PROSPERO_country, ',')) %>%
filter(., tool!="No") %>%
filter(., new_unitofanalysis!="No") %>%
filter(., measure!="Diagnostic Accuracy") %>%
filter(., measure!="BMJ Critical Appraisal Tools") %>%
filter(., measure!="CEBM Critical Appraisal Tools") %>%
filter(., measure!="JBI Critical Appraisal Tools") %>%
filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")
collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
Unknown levels in `f`: Health education and health behavior research studies, Quantitative Research (Public Health Research)
ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) +
geom_bar(stat='count', aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
#coord_flip()+
theme(axis.text.x=element_text(size=6))+
theme(axis.text.y=element_text(size=6))+
scale_x_discrete(labels = c("2011", "2012", "2013", "2014", "2015", "2016", "2017"))+
facet_wrap(~measure)+
theme(legend.position="none")

#### barplots
#### topic X measure AND unitOfAnalisys
#### UNIQUE COUNTRIES
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>%
filter(., unique_country=="unique") %>%
filter(., tool!="No") %>%
filter(., new_unitofanalysis!="No") %>%
filter(., measure!="Diagnostic Accuracy") %>%
filter(., measure!="BMJ Critical Appraisal Tools") %>%
filter(., measure!="CEBM Critical Appraisal Tools") %>%
filter(., measure!="JBI Critical Appraisal Tools") %>%
filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")
unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=measure,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) +
geom_bar(stat='count', aes(x=measure,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
#coord_flip()+
theme(axis.text.x=element_text(size=6))+
theme(axis.text.y=element_text(size=6))+
facet_wrap(~group)+
theme(legend.position="none")
#### COLLABORATIVE COUNTRIES
# unique_curated_dataset_and_tools<-curated_dataset_and_tools %>%
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>%
filter(str_detect(PROSPERO_country, ',')) %>%
filter(., tool!="No") %>%
filter(., new_unitofanalysis!="No") %>%
filter(., measure!="Diagnostic Accuracy") %>%
filter(., measure!="BMJ Critical Appraisal Tools") %>%
filter(., measure!="CEBM Critical Appraisal Tools") %>%
filter(., measure!="JBI Critical Appraisal Tools") %>%
filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")
collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) +
geom_bar(stat='count', aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
#coord_flip()+
theme(axis.text.x=element_text(size=6))+
theme(axis.text.y=element_text(size=6))+
scale_x_discrete(labels = c("2011", "2012", "2013", "2014", "2015", "2016", "2017"))+
facet_wrap(~measure)+
theme(legend.position="none")





### rank comare between continents
my_theme <- function() {
# Colors
color.background = "white"
color.text = "#22211d"
# Begin construction of chart
theme_bw(base_size=15) +
# Format background colors
theme(panel.background = element_rect(fill=color.background, color=color.background)) +
theme(plot.background = element_rect(fill=color.background, color=color.background)) +
theme(panel.border = element_rect(color=color.background)) +
theme(strip.background = element_rect(fill=color.background, color=color.background)) +
# Format the grid
theme(panel.grid.major.y = element_blank()) +
theme(panel.grid.minor.y = element_blank()) +
theme(axis.ticks = element_blank()) +
# Format the legend
theme(legend.position = "none") +
# Format title and axis labels
theme(plot.title = element_text(color=color.text, size=20, face = "bold")) +
theme(axis.title.x = element_text(size=14, color="black", face = "bold")) +
theme(axis.title.y = element_text(size=14, color="black", face = "bold", vjust=1.25)) +
theme(axis.text.x = element_text(size=10, vjust=0.5, hjust=0.5, color = color.text)) +
theme(axis.text.y = element_text(size=10, color = color.text)) +
theme(strip.text = element_text(face = "bold")) +
# Plot margins
theme(plot.margin = unit(c(0.35, 0.2, 0.3, 0.35), "cm"))
}
labels<-as.vector(levels(rank_data$disease))
rank_compare<-read.csv2(file="rank_compare.csv")
rank_data <- rank_compare %>% gather(continent,Rank,-disease)
ggplotColors <- function(g){
d <- 360/g
h <- cumsum(c(15, rep(d,g - 1)))
hcl(h = h, c = 100, l = 65)
}





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---
title: "Appropriateness of tool usage for evidence critical appraisal: A meta-epidemiological study of 20,000 PROSPERO records of non-Cochrane systematic reviews"
output: html_notebook
  
---

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter*. 

```{r}
### packages ----------
library("tidyverse")
library("stringr")
```

```{r}
#### datasets ----------
### dataset con las RoB tools usada en los protocols
dataset_raw<-read.csv(file="Prospero_Final File.csv", sep=";")

### dataset del articulo primero de PROSPERO
dataset_PROSPERO_first<-read.csv2("protocol_dataset_curated_17JUN2018.csv", sep=";")
dataset_PROSPERO_first<-dataset_PROSPERO_first[,c(2,3,7:14,16)]

###  merge dataset_raw + dataset_first BY CRDs
all_dataset_prospero2<-merge(dataset_raw, dataset_PROSPERO_first, by.x="crds", by.y="CDR_PROSPERO_records", all.y=TRUE, all.x=TRUE)

### final variable selection  
dataset_PROSPERO2_final<-all_dataset_prospero2[,c(1,6,9:61, 85:93)]
lapply(dataset_PROSPERO2_final[,c(5:55)], levels)
fct_collapse(dataset_PROSPERO2_final$BMJ.checklist, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$CASP, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$CHEC.list, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$CHEERS, 'Yes' = c('YES', 'yes')) 



fct_collapse(dataset_PROSPERO2_final$CMS.MCMS, 'Yes' = c('YES ', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$COCHRANE.RISK.OF.BIAS, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$CONSORT, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$COREQ, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$COSMIN.CHECLIST, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$Downs.and.Black, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$Drummond.checklist, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$EPHPP, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$GRADE, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$ISPOR, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$JADAD, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$JBI, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$MCMASTER, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$MERSQI, 'Yes' = c('YES ', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$MINORS, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$NEWCASTLE.OTTAWA, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$OCEBM.CEBM, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$OQAQ, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$PEDRO.SCALE, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$Phillips.checklist, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$PRISMA, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$QUADAS.QUADAS.2, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$RE.AIM, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$REBIP, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$RoBANS, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$SIGN...SIGN.50, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$STARD, 'Yes' = c('YES', 'yes')) 
fct_collapse(dataset_PROSPERO2_final$STROBE, 'Yes' = c('YES', 'yes')) 

attach(dataset_PROSPERO2_final)

dataset_PROSPERO2_final <- dataset_PROSPERO2_final %>%
  mutate(AMSTAR = fct_recode(AMSTAR, 
                             "Yes" = "YES",
                             "Yes" = "Yes",
                             "Yes" = "yes",
                             "No" = ""))
var<-names(dataset_PROSPERO2_final[,c(5:55)])
variables<-unlist(var)
dataset_PROSPERO2_final %>% mutate_at(.vars = #vars(dataset_PROSPERO2_final[,c(5:55)]), 
                                        as.character(variables),
                                      .funs=forcats::fct_recode,
                                      "Yes" = "YES",
                                      "Yes" = "Yes",
                                      "Yes" = "yes",
                                      "No" = "")

levels(dataset_PROSPERO2_final$BMJ.checklist)


#######  simplificarniveles de topic a una más general
dataset_PROSPERO2_final<-dataset_PROSPERO2_final %>% 
  mutate(Topic_gral=recode(Topic, c("cancer" = "Cancer")))
```


```{r}
####  extraer CRDs del campo Citation con regex

prosperoID<-"[C][R][D]([0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9])"
dataset_raw$crds<-str_extract(dataset_raw$Citation, prosperoID)

```


```{r}
curated_dataset<-read.csv2(file="dataset_PROSPERO2_final.csv")
curated_dataset_min<-curated_dataset[,c(2,4,5:10,161:171)]
tools<-read.csv2(file="crd_appraisal_tools.csv")
curated_dataset_and_tools<-merge(tools, curated_dataset_min, by.x="crds", by.y="crds", all.x=TRUE)
```

```{r}
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(., unique_country=="unique") %>%
  filter(., tool!="No")
ggplot(na.omit(unique_curated_dataset_and_tools[,c(3:6,13,14,21)]),aes(x=Topics_general,y=tool, color=measure)) + 
  geom_count(aes(x=Topics_general,y=tool, color=measure))+
  coord_flip()+
  theme(axis.text.y=element_text(size=4))+
  #facet_wrap(~unique_continent)+
  theme(legend.position="none")
```

```{r}

table_topics <- as.data.frame(table(curated_dataset_and_tools$Topics_general))
table_topics$Freq <- c(scale(table_topics$Freq))
curated_dataset_and_tools<-merge(curated_dataset_and_tools, table_topics, by.x="Topics_general", by.y = "Var1", all.x = TRUE)
colnames(curated_dataset_and_tools)[which(colnames(curated_dataset_and_tools) == 'Freq')] <- 'Freq_topics'



curated_dataset_and_tools <- curated_dataset_and_tools %>% 
  mutate(new_unitofanalysis = forcats::fct_recode(unitofanalysis, 
            'RCTs' = "RCTs", 'Non-RCTs and Observational Studies' = "Non-RCTs and Observational studies", 'Non-RCTs and Observational Studies' = "Non-RCTs or Observational Studies",
            'RCTs and Observational Studies' = "RCTs and observational studies",
            'RCTs and Observational Studies' = "RCTs and Observational studies", 
            #'Public Health Research' = "Quantitative Research (Public Health Research)", 
            'Qualitative research studies' = "Qualitative research",
            'Qualitative research studies' = "Qualitative research studies",
            'Mixed methods studies' = "Mixed methods studies", 
            'Patient-Reported Outcome Measures Studies' = "Patient-Reported Outcome Measures",
            
            'SIGN...SIGN.50 Critical Appraisal Tools' = "SIGN...SIGN.50 Critical Appraisal Tools",
            'BMJ Critical Appraisal Tools' = "BMJ Critical Appraisal Tools",   
            'CEBM Critical Appraisal Tools' = "CEBM Critical Appraisal Tools",  
            'JBI Critical Appraisal Tools' = "JBI Critical Appraisal Tools", 
           'Critical Appraisal of Clinical Practice Guidelines' = "Critical Appraisal of Clinical Practice Guidelines",
           'Critical Appraisal of Systematic Reviews' = "Critical Appraisal of Systematic Reviews", 
           'Diagnostic Accuracy Studies' = "Diagnostic Accuracy Studies",                          
           'Economic Evaluation Studies' = "Economic evaluation",                                  
           #'Health education and health behavior research studies' = "Health education and health behavior research studies",
           'Medical education' = "Medical education",   
           'No' = "Quality",
           'No' = "No"))

#### Unique Countries
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(., unique_country=="unique") %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,y=tool, fill=new_unitofanalysis, color=new_unitofanalysis, shape=measure, alpha=Freq_topics)) + 
  geom_count(aes(x=Topics_general,y=tool, fill=new_unitofanalysis,  color=new_unitofanalysis,   shape=measure,alpha=Freq_topics))+
  scale_shape(name = "Assessment objective")+
  scale_shape_manual(values=c(24,21,22,25))+
  coord_flip()+
  theme(axis.text.y=element_text(angle=0, size=6))+
  theme(axis.text.x=element_text(angle=90, size=6, hjust=1))+
  facet_wrap(~measure)+
  theme(legend.position="none")


ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=tool, fill=unique_continent, color=unique_continent)) + 
  geom_bar(aes(x=tool, fill=unique_continent,  color=unique_continent))+
  scale_color_manual(values=c("#6495ED", "#FF3030", "#CDAA7D", "#FFD700", "#458B00"))+
  scale_fill_manual(values=c("#6495ED", "#FF3030", "#CDAA7D", "#FFD700", "#458B00"))+
  coord_flip()+
  theme(axis.text.y=element_text(angle=0, size=6))+
  theme(axis.text.x=element_text(angle=90, size=6, hjust=1))+
  facet_wrap(~ALL_year)+
  theme(legend.position="none")
```

```{r}
#### Collaborative Countries
#### no parece muy diferente al plot con unique!!!!  :-(
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  #filter(., unique_country=="unique") %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))
ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,y=tool, fill=new_unitofanalysis, color=new_unitofanalysis, shape=measure, alpha=Freq_topics)) + 
  geom_count(aes(x=Topics_general,y=tool, fill=new_unitofanalysis,  color=new_unitofanalysis,   shape=measure,alpha=Freq_topics))+
  scale_shape(name = "Assessment objective")+
  scale_shape_manual(values=c(24,21,22,25))+
  coord_flip()+
  theme(axis.text.y=element_text(angle=0, size=6))+
  theme(axis.text.x=element_text(angle=90, size=6, hjust=1))+
  facet_wrap(~measure)+
  theme(legend.position="none")
```

```{r TOPICS BY measure AND unit of analysis}
#### barplots 
#### topic X measure AND unitOfAnalisys
#### UNIQUE COUNTRIES
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(., unique_country=="unique") %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))

ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=10))+
  facet_wrap(~measure)+
  theme(legend.position="none")


#### COLLABORATIVE COUNTRIES
# unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(str_detect(PROSPERO_country, ',')) %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))

ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics, drop = FALSE))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  coord_flip()+
  scale_y_continuous(limits = c(0, 500))+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=10))+
  facet_wrap(~measure)+
  theme(legend.position="none")

```


```{r}
####  
#### newunitOFanalysis X measure AND year
#### UNIQUE COUNTRIES
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(., unique_country=="unique") %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))

ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=new_unitofanalysis,fill=measure, color=measure, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=new_unitofanalysis,fill=measure, color=measure, alpha=Freq_topics))+
  # scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  # scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_color_manual(values=c(c("#66CDAA", "#6495ED", "#F08080")))+
  scale_fill_manual(values=c("#66CDAA", "#6495ED", "#F08080"))+
  coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=10))+
  facet_wrap(group~measure)+
  theme(legend.position="none")

unique_curated_dataset_and_tools$ALL_year<-as.factor(unique_curated_dataset_and_tools$ALL_year)
ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=new_unitofanalysis,fill=ALL_year, color=ALL_year)) + 
  geom_bar(stat='count', aes(x=new_unitofanalysis,fill=ALL_year, color=ALL_year))+
  scale_color_manual(values=c("#C1FFC1", "#B4EEB4", "#9BCD9B", "#8FBC8F", "#698B69", "#6E8B3D", "#556B2F"))+
  scale_fill_manual(values=c("#C1FFC1", "#B4EEB4", "#9BCD9B", "#8FBC8F", "#698B69", "#6E8B3D", "#556B2F"))+
  coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=10))+
  facet_wrap(~measure)+
  theme(legend.position="none")
```

```{r unit of analysis BY measure AND YEAR}
#### barplots 
#### topic X measure AND unitOfAnalisys
#### UNIQUE COUNTRIES
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(., unique_country=="unique") %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))

ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  #coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=6))+
  scale_x_discrete(labels = c("2011", "2012", "2013", "2014", "2015", "2016", "2017"))+
  facet_wrap(~measure)+
  theme(legend.position="none")


#### COLLABORATIVE COUNTRIES
# unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(str_detect(PROSPERO_country, ',')) %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))


ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  #coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=6))+
  scale_x_discrete(labels = c("2011", "2012", "2013", "2014", "2015", "2016", "2017"))+
  facet_wrap(~measure)+
  theme(legend.position="none")

```

```{r unit of analysis BY measure AND group}
#### barplots 
#### topic X measure AND unitOfAnalisys
#### UNIQUE COUNTRIES
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(., unique_country=="unique") %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))

ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=measure,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=measure,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  #coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=6))+
  facet_wrap(~group)+
  theme(legend.position="none")


#### COLLABORATIVE COUNTRIES
# unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(str_detect(PROSPERO_country, ',')) %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="BMJ Critical Appraisal Tools") %>%
  filter(., measure!="CEBM Critical Appraisal Tools") %>%
  filter(., measure!="JBI Critical Appraisal Tools") %>%
  filter(., measure!="SIGN...SIGN.50 Critical Appraisal Tools")

collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "Critical Appraisal of Clinical Practice Guidelines", "Critical Appraisal of Systematic Reviews", "Diagnostic Accuracy Studies","Economic Evaluation Studies","Health education and health behavior research studies", "RCTs", "Non-RCTs and Observational Studies", "RCTs and Observational Studies", "Quantitative Research (Public Health Research)", "Qualitative research studies", "Patient-Reported Outcome Measures Studies", "Mixed methods studies", "Medical education","BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools"))


ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=as.factor(ALL_year),fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  #coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=6))+
  scale_x_discrete(labels = c("2011", "2012", "2013", "2014", "2015", "2016", "2017"))+
  facet_wrap(~measure)+
  theme(legend.position="none")

```

```{r}
#### UNIQUE
#### barplots Critical Appraisal Tools
unique_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(., unique_country=="unique") %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="No") %>%
  filter(., measure!="Reporting") %>%
  filter(., measure!="Quality") %>%
  filter(., measure!="RoB") %>%
  filter(., (measure=="BMJ Critical Appraisal Tools" | measure=="CEBM Critical Appraisal Tools" |measure=="JBI Critical Appraisal Tools" |measure=="SIGN...SIGN.50 Critical Appraisal Tools" ))

unique_curated_dataset_and_tools<-unique_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools")

ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=10))+
  facet_wrap(~measure)+
  theme(legend.position="none")


ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,fill=measure, color=measure, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=Topics_general,fill=measure, color=measure, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=10))+
  facet_wrap(~unique_continent)+
  theme(legend.position="none")
```


```{r}
#### COLLABORATIVE
#### barplots Critical Appraisal Tools
collaborative_curated_dataset_and_tools<-curated_dataset_and_tools %>% 
  filter(str_detect(PROSPERO_country, ',')) %>%
  filter(., tool!="No") %>%
  filter(., new_unitofanalysis!="No") %>%
  filter(., measure!="Diagnostic Accuracy") %>%
  filter(., measure!="No") %>%
  filter(., measure!="Reporting") %>%
  filter(., measure!="Quality") %>%
  filter(., measure!="RoB") %>%
  filter(., (measure=="BMJ Critical Appraisal Tools" | measure=="CEBM Critical Appraisal Tools" |measure=="JBI Critical Appraisal Tools" |measure=="SIGN...SIGN.50 Critical Appraisal Tools" ))

collaborative_curated_dataset_and_tools<-collaborative_curated_dataset_and_tools %>%
  mutate(new_unitofanalysis = fct_relevel(new_unitofanalysis, "BMJ Critical Appraisal Tools" ,"CEBM Critical Appraisal Tools","JBI Critical Appraisal Tools","SIGN...SIGN.50 Critical Appraisal Tools")

ggplot(na.omit(collaborative_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=Topics_general,fill=new_unitofanalysis, color=new_unitofanalysis, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=10))+
  facet_wrap(~unique_continent)+
  theme(legend.position="none")


ggplot(na.omit(unique_curated_dataset_and_tools[,c(1,3:6,13,14,21,24,25)]),aes(x=Topics_general,fill=measure, color=measure, alpha=Freq_topics)) + 
  geom_bar(stat='count', aes(x=Topics_general,fill=measure, color=measure, alpha=Freq_topics))+
  scale_color_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  scale_fill_manual(values=c("#838B8B", "#C1CDCD", "#F0E68C", "#FF8C00", "#1874CD", "#00BFFF", "#458B74", "#66CDAA", "#B4EEB4", "#8B7355", "#CDAA7D", "#FFD39B", "#F5F5DC"))+
  coord_flip()+
  theme(axis.text.x=element_text(size=6))+
  theme(axis.text.y=element_text(size=10))+
  facet_wrap(~unique_continent)+
  theme(legend.position="none")
```

```{r}
names(dataset_PROSPERO2_final)
unique_curated_dataset<-curated_dataset %>% 
  filter(., unique_country=="unique")
ggplot(na.omit(unique_curated_dataset[,c(4,12,70,71)]),aes(x=Topics_general,fill=AMSTAR)) + 
  geom_bar(stat='count',aes(x=Topics_general,fill=AMSTAR))+
  coord_flip()+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~unique_continent)+
  theme(legend.position="none")
```

```{r}
####  PLOTS
# library("gghighlight")
curated_dataset_order <- as.data.frame(table(curated_dataset[,c(4)]))
curated_dataset_order_final<-merge(curated_dataset, curated_dataset_order, by.x="Topics_general", by.y = "Var1", all.x = TRUE)
curated_dataset_order_final<-arrange(curated_dataset_order_final,desc(Freq))
final_order<-curated_dataset_order_final[order(curated_dataset_order_final$Freq),c(1,64,72)]
newdata <- curated_dataset_order_final[order(-curated_dataset_order_final$Freq),] 

ggplot(na.omit(newdata),aes(x=ALL_year,color=Topics_general)) + 
  geom_point(stat = 'count') +
  geom_line(stat='count',aes(x=ALL_year,color=Topics_general))+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~Topics_general)+
  theme(legend.position="none")+
  xlab("Year") +
  ylab("nº protocols")+
  scale_x_continuous(limits=c(2011, 2017), breaks = c(2011,2012,2013,2014,2015,2016,2017))

  # scale_x_continuous(limits=c(2011, 2017), breaks = c(2011,2012,2013,2014,2015,2016,2017))+
  # xlab("Year") +
  # ylab("nº protocols")+
  # ggtitle("SRs protocols by journal")

ggplot(curated_dataset,aes(x=ALL_year,color=methodological.quality)) + 
  geom_point(stat = 'count') +
  geom_line(stat='count',aes(x=ALL_year,color=methodological.quality))+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~methodological.quality)+
  theme(legend.position="none")+
  xlab("Year") +
  ylab("nº protocols")

ggplot(curated_dataset,aes(x=ALL_year,color=quality.of.studies)) + 
  geom_point(stat = 'count') +
  geom_line(stat='count',aes(x=ALL_year,color=quality.of.studies))+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~quality.of.studies)+
  theme(legend.position="none")

ggplot(curated_dataset,aes(x=ALL_year,color=risk.of.bias)) + 
  geom_point(stat = 'count') +
  geom_line(stat='count',aes(x=ALL_year,color=risk.of.bias))+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~risk.of.bias)+
  theme(legend.position="none")

ggplot(curated_dataset,aes(x=ALL_year,color=two_reviewers_OR_two_authors)) + 
  geom_point(stat = 'count') +
  geom_line(stat='count',aes(x=ALL_year,color=two_reviewers_OR_two_authors))+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~two_reviewers_OR_two_authors)+
  theme(legend.position="none")

ggplot(curated_dataset,aes(x=ALL_year,color=RoB_field_information)) + 
  geom_point(stat = 'count') +
  geom_line(stat='count',aes(x=ALL_year,color=RoB_field_information))+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~RoB_field_information)+
  theme(legend.position="none")

unique_curated_dataset<-curated_dataset %>% filter(., unique_country=="unique")
ggplot(unique_curated_dataset,aes(x=Topics_general,color=PROSPERO_country)) + 
  geom_bar(stat='count',aes(x=Topics_general,color=PROSPERO_country))+
  coord_flip()+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~PROSPERO_country)+
  theme(legend.position="none")

###  topics BY countries colored BY continent
unique_curated_dataset<-curated_dataset %>% filter(., unique_country=="unique")
ggplot(unique_curated_dataset,aes(x=Topics_general,color=unique_continent)) + 
  geom_bar(stat='count',aes(x=Topics_general,color=unique_continent))+
  coord_flip()+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~PROSPERO_country)+
  theme(legend.position="none")


###  topics BY continent colored BY continent
unique_curated_dataset<-curated_dataset %>% 
  filter(., unique_country=="unique")
ggplot(na.omit(unique_curated_dataset[,c(4,70,71)]),aes(x=Topics_general,color=unique_continent)) + 
  geom_bar(stat='count',aes(x=Topics_general,color=unique_continent))+
  coord_flip()+
  theme(axis.text.y=element_text(size=4))+
  facet_wrap(~unique_continent)+
  theme(legend.position="none")
```

```{r}
### rank comare between continents
my_theme <- function() {
  
  # Colors
  color.background = "white"
  color.text = "#22211d"
  
  # Begin construction of chart
  theme_bw(base_size=15) +
    
    # Format background colors
    theme(panel.background = element_rect(fill=color.background, color=color.background)) +
    theme(plot.background  = element_rect(fill=color.background, color=color.background)) +
    theme(panel.border     = element_rect(color=color.background)) +
    theme(strip.background = element_rect(fill=color.background, color=color.background)) +
    
    # Format the grid
    theme(panel.grid.major.y = element_blank()) +
    theme(panel.grid.minor.y = element_blank()) +
    theme(axis.ticks       = element_blank()) +
    
    # Format the legend
    theme(legend.position = "none") +
    
    # Format title and axis labels
    theme(plot.title       = element_text(color=color.text, size=20, face = "bold")) +
    theme(axis.title.x     = element_text(size=14, color="black", face = "bold")) +
    theme(axis.title.y     = element_text(size=14, color="black", face = "bold", vjust=1.25)) +
    theme(axis.text.x      = element_text(size=10, vjust=0.5, hjust=0.5, color = color.text)) +
    theme(axis.text.y      = element_text(size=10, color = color.text)) +
    theme(strip.text       = element_text(face = "bold")) +
    
    # Plot margins
    theme(plot.margin = unit(c(0.35, 0.2, 0.3, 0.35), "cm"))
}
labels<-as.vector(levels(rank_data$disease))
rank_compare<-read.csv2(file="rank_compare.csv")
rank_data <- rank_compare %>% gather(continent,Rank,-disease)
ggplotColors <- function(g){
  d <- 360/g
  h <- cumsum(c(15, rep(d,g - 1)))
  hcl(h = h, c = 100, l = 65)
}

```

```{r}

#grep("blue",colors())
#  TOP10 Europe 
ggplot(data = rank_data, aes(x = continent, y = Rank, group = disease)) +
  geom_line(aes(color = disease, alpha = 1), size = 1) +
  geom_point(aes(color = disease, alpha = 1), size = 2) +
  scale_colour_manual(values = c(8,2,2,8,8,8,8,2,8,8,8,2,8,8,8,2,8,8,2,8,8,8,2,8,8,2,8,8,2,8,8,8,8,2,8))+
  scale_x_discrete(expand = c(0.85,0), limits = c("Europe","America","Asia","South.Asia.and.Oceania","Africa"))+
  scale_y_reverse(breaks = 1:35)+
  theme(legend.position = "none") +
  labs(x = "",
       y = "",
       title = "protocols for SRs by topic \ncomparing rankings by continent") +
  my_theme() + 
  geom_text(data =rank_data,aes(y=Rank,x=0.6,label=c(""),hjust="right"))
```

```{r}

#  TOP10 America
ggplot(data = rank_data, aes(x = continent, y = Rank, group = disease)) +
  geom_line(aes(color = disease, alpha = 1), size = 1) +
  geom_point(aes(color = disease, alpha = 1), size = 2) +
  scale_colour_manual(values = c(8,1,1,8,8,8,8,1,8,8,8,1,8,8,8,1,8,8,1,8,4,8,1,8,8,1,8,8,1,8,8,8,8,1,8))+
  scale_x_discrete(expand = c(0.85,0), limits = c("Europe","America","Asia","South.Asia.and.Oceania","Africa"))+
  scale_y_reverse(breaks = 1:35)+
  theme(legend.position = "none") +
  labs(x = "",
       y = "",
       title = "protocols for SRs by topic \ncomparing rankings by continent") +
  my_theme() + 
  geom_text(data =rank_data,aes(y=Rank,x=0.6,label=c(""),hjust="right"))
```

```{r}

#  TOP10 Asia
ggplot(data = rank_data, aes(x = continent, y = Rank, group = disease)) +
  geom_line(aes(color = disease, alpha = 1), size = 1) +
  geom_point(aes(color = disease, alpha = 1), size = 2) +
  scale_colour_manual(values = c(3,1,1,8,8,8,8,1,8,8,8,1,8,8,8,1,8,3,1,8,3,8,1,8,8,1,8,8,1,8,8,8,8,1,8))+
  scale_x_discrete(expand = c(0.85,0), limits = c("Europe","America","Asia","South.Asia.and.Oceania","Africa"))+
  scale_y_reverse(breaks = 1:35)+
  theme(legend.position = "none") +
  labs(x = "",
       y = "",
       title = "protocols for SRs by topic \ncomparing rankings by continent") +
  my_theme() + 
  geom_text(data =rank_data,aes(y=Rank,x=0.6,label=c(""),hjust="right"))

```

```{r}

#  TOP10 Oceania
ggplot(data = rank_data, aes(x = continent, y = Rank, group = disease)) +
  geom_line(aes(color = disease, alpha = 1), size = 1) +
  geom_point(aes(color = disease, alpha = 1), size = 2) +
  scale_colour_manual(values = c(8,1,1,8,8,8,8,1,8,8,8,1,8,8,8,1,8,8,1,8,8,8,1,8,8,1,8,8,1,8,8,8,8,1,8))+
  scale_x_discrete(expand = c(0.85,0), limits = c("Europe","America","Asia","South.Asia.and.Oceania","Africa"))+
  scale_y_reverse(breaks = 1:35)+
  theme(legend.position = "none") +
  labs(x = "",
       y = "",
       title = "protocols for SRs by topic \ncomparing rankings by continent") +
  my_theme() + 
  geom_text(data =rank_data,aes(y=Rank,x=0.6,label=c(""),hjust="right"))
```

```{r}
#  TOP10 Africa
ggplot(data = rank_data, aes(x = continent, y = Rank, group = disease)) +
  geom_line(aes(color = disease, alpha = 1), size = 1) +
  geom_point(aes(color = disease, alpha = 1), size = 2) +
  scale_colour_manual(values = c(8,1,1,8,8,8,8,1,8,8,8,1,8,8,8,1,8,8,1,8,15,8,1,8,8,1,8,8,1,8,8,8,8,1,8))+
  scale_x_discrete(expand = c(0.85,0), limits = c("Europe","America","Asia","South.Asia.and.Oceania","Africa"))+
  scale_y_reverse(breaks = 1:35)+
  theme(legend.position = "none") +
  labs(x = "",
       y = "",
       title = "protocols for SRs by topic \ncomparing rankings by continent") +
  my_theme() + 
  geom_text(data =rank_data,aes(y=Rank,x=0.6,label=c(""),hjust="right"))
```



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